Argument Selfloop

Community

WILLOSCAR

argument-selfloop

Build a final, machine-readable argument snapshot from current H3 sections and refresh section fingerprints before merge.

Overview

Publisher	WILLOSCAR
Repository	`research-units-pipeline-skills`
Skill name	`argument-selfloop`
Stars	513
Forks	39
Bundled files	1
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by WILLOSCAR on GitHub. Read the source before you install it.

Installation

Install the Argument Selfloop AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/argument-selfloop
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/argument-selfloop

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/WILLOSCAR/research-units-pipeline-skills.git /tmp/research-units-pipeline-skills
mkdir -p .claude/skills
cp -r /tmp/research-units-pipeline-skills/.codex/skills/argument-selfloop .claude/skills/argument-selfloop

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Argument Selfloop in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Argument Selfloop on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Argument Selfloop is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Argument Snapshot

Triggers & routing

Trigger: argument snapshot, section ledger, consistency contract, final section manifest, 论证快照, 一致性契约.
Use when: section-level mutations are complete and the merge needs a current argument ledger plus content hashes.

Create the final C5 snapshot consumed by merge and later audit.

Despite the historical Skill name, the current implementation is not an autonomous rewrite loop. It reads the final H3 files, assigns bounded structural move labels, writes a compact consistency contract, refreshes section hashes, and fails when required prose is absent or too thin.

Position In The Workflow

text
section writing
-> style and numeric hygiene
-> logic polish
-> paragraph compaction
-> argument snapshot
-> optional transitions
-> merge

Running the snapshot after every section mutator prevents the ledger and manifest from describing an earlier draft.

Inputs

sections/
outline/outline.yml
queries.md
output/PARAGRAPH_CURATION_REPORT.md

Outputs

output/ARGUMENT_SELFLOOP_TODO.md
output/SECTION_ARGUMENT_SUMMARIES.jsonl
output/ARGUMENT_SKELETON.md
refreshed sections/sections_manifest.jsonl

SECTION_ARGUMENT_SUMMARIES.jsonl contains one record per expected H3 and one record per paragraph. Current move labels are deterministic signals drawn from:

text
setup, thesis, contrast, evidence, evaluation, limitation, synthesis, takeaway

They make section shape inspectable; they do not prove that an argument is scientifically valid. ARGUMENT_SKELETON.md contains a ## Consistency Contract and a chapter-level map. It is a writer-facing audit artifact, never reader-facing paper content.

PASS Contract

every expected H3 exists;
every H3 meets the profile paragraph floor;
each paragraph has at least one allowed move;
ARGUMENT_SKELETON.md contains ## Consistency Contract;
sections_manifest.jsonl carries current bytes and sha256 values.

On FAIL, inspect ARGUMENT_SELFLOOP_TODO.md and route the named section back to its writer or evidence owner. This Skill does not apply the repair itself.

Run

bash
uv run python .codex/skills/argument-selfloop/scripts/run.py \
  --workspace workspaces/<name>

Optional runner fields are --unit-id, --inputs, --outputs, and --checkpoint.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

scripts/run.py8 KB

Frequently asked questions

What does the Argument Selfloop AI skill do?

Build a final, machine-readable argument snapshot from current H3 sections and refresh section fingerprints before merge.

Why use Argument Selfloop on TypingMind?

Because you install it once and use it with any model. Argument Selfloop is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Argument Selfloop in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/argument-selfloop. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Argument Selfloop?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Argument Selfloop?

As many as you like. As long as a model supports skills, you can use Argument Selfloop with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Argument Selfloop AI skill free?

It is published on GitHub by WILLOSCAR. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Feishu Perm

openclaw

OrganizationPopular

Feishu collaborator and permission workflows. Activate when the user explicitly asks to inspect or change sharing, permissions, or collaborators.

Block Kit

Use proactively for structured or interactive Slack replies, and when asked to author or validate native Slack Block Kit JSON.

1password

Set up and use 1Password CLI for sign-in, desktop integration, and reading or injecting secrets.

Apple Reminders

List, add, edit, complete, or delete Apple Reminders and reminder lists via remindctl.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Argument Snapshot

Triggers & routing

Position In The Workflow

Inputs

Outputs

PASS Contract

Run

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now